A Bayesian Distance-Weighted Ising Model for Task-Related Regional Analysis of Neuroimaging Data
Neuroimaging analysis commonly involves classifying cognitive conditions, identifying task-relevant brain regions, and examining functional connectivity. These objectives are typically addressed separately, without a model-based framework. In contrast, we propose a Bayesian distance-weighted Ising model that integrates classification, regional importance, and connectivity analysis. The model captures dependence among task labels based on connectivities between functional signals, while region-of-interest (ROI)-specific interaction parameters account for spatial heterogeneity and quantify regional contributions to task discrimination. However, inference is challenging because the signals may be observed on unequal time grids and the likelihood function contains an intractable normalising constant. To address this, we use a basis expansion and an auxiliary variable Markov chain Monte Carlo algorithm. We apply the method to functional near-infrared spectroscopy data from younger and older adults, comparing resting-state and task-related activity. The model achieves high classification accuracy and reveals age-dependent patterns of regional importance and connectivity. Among younger adults, the left orbitofrontal cortex contributes more strongly to task discrimination than the dorsolateral prefrontal and frontopolar cortices, whereas regional differences are less evident among older adults. Older adults also exhibit greater bilateral connectivity between corresponding ROIs, consistent with reduced hemispheric lateralisation.